Development and Validation of a Combined Caprini Score and D-Dimer Model for Predicting Venous Thromboembolism in...

ObjectiveThis study aimed to develop and validate a predictive model for venous thromboembolism (VTE) risk in high-altitude hospitalized patients by combining the Caprini score with D-dimer levels and assessing the impact of platelet count in a unique cohort.MethodsWe enrolled 1002 hospitalized patients at Lhasa People's Hospital (3650 m). Clinical data, including Caprini score, D-dimer, and platelet count, were collected at admission. Logistic regression identified predictors of VTE, and model performance was evaluated using ROC curves, with sensitivity analyses excluding patients on thromboprophylaxis. External validation was performed in 400 patients from the Tibet Military Region General Hospital using the same protocol.ResultsAmong the 1002 hospitalized patients, 135 (13.5%) developed venous thromboembolism (VTE), consisting of 61 cases of deep vein thrombosis (DVT), 42 cases of pulmonary embolism (PE), and 32 cases of both DVT and PE. Multivariate analysis identified Caprini score, D-dimer levels, platelet count, and C-reactive protein (CRP) as independent predictors of VTE. However, platelet count and CRP were excluded from the final predictive model due to their limited incremental predictive value. The combined Caprini-D-dimer model demonstrated superior predictive accuracy with an area under the curve (AUC) of 0.89, compared to 0.84 for Caprini score and 0.81 for D-dimer alone. Sensitivity analysis excluding patients who received thromboprophylaxis improved the AUC to 0.904. External validation in a cohort of 400 patients confirmed the model's robustness with an AUC of 0.86.ConclusionThe combined Caprini score and D-dimer model provides a more accurate tool for predicting VTE risk in high-altitude hospitalized patients compared to individual predictors. This model offers enhanced sensitivity and specificity, making it a valuable tool for clinical decision-making in high-altitude settings. The model maintained high predictive performance after excluding patients receiving prophylactic anticoagulation, suggesting robustness across different clinical scenarios.